Prediction of field intensity in mine tunnel based on LS-SVM
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摘要: 针对目前矿井巷道场强预测精度低的问题,提出采用最小二乘支持向量机方法建立预测模型,以某巷道实测数据作为训练样本,对矿井巷道场强进行预测;详细分析了训练集构造和参数选择对预测效果的影响。仿真结果表明,LS-SVM预测模型较双斜率模型和对数校正模型具有更高的预测精度。Abstract: For problem of low accuracy of current prediction of field intensity in mine tunnel, a prediction model based on the least squares support vector machine method was proposed to predict the field intensity in mine tunnel by taking measured data of a tunnel as training sample. Influence of training set construction and parameters selection on prediction effect were analyzed in details. The simulation results show that the LS-SVM prediction model has higher prediction accuracy than dual-slope model and logarithmic correction model.
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Key words:
- mine tunnel /
- field intensity /
- prediction model /
- least squares support vector machine
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